Wenhao Li

On-policy Reinforcement Fine-tuning with Offline reward for Multi-step Embodied Planning

Experimental results show that ORBIT achieves SOTA performance on EB-ALFRED, outper-forming all closed-source and online-RL-based methods, while being substantially more effi-cient in training speed and computational cost, remaining robust to sub-optimal expert trajectories, and exhibiting strong generalization to unseen environments.